import chart_studio.plotly as py
import plotly.graph_objects as go
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
np.random.seed(42)
We will start our robotic arm in the standard position as we can see below:
Para a construção do nosso manipulador, inicializaremos ele na posição padrão.
from src.robot import Manipulator, Joint
joint1 = Joint(100, 0, 90, 90)
joint2 = Joint(0, 50, 0, 0, link=joint1)
joint3 = Joint(0, 50, 0, 0, link=joint2)
robot = Manipulator([joint1, joint2, joint3])
robot.forward_kinematics()
robot.static_plot()
print('Final Matrix:\n', np.round(joint3.updated_matrix))
Final Matrix: [[ 0. -0. 1. 0.] [ 1. 0. -0. 100.] [ 0. 1. 0. 100.] [ 0. 0. 0. 1.]]
robot.get_X(), robot.get_Y(), robot.get_Z()
(6.123233995736766e-15, 100.0, 100.0)
robot.update_theta(0, 0)
robot.update_theta(1, -90)
robot.update_theta(2, -10)
robot.forward_kinematics()
robot.static_plot()
robot.get_X(), robot.get_Y(), robot.get_Z()
(-8.68240888334651, -6.076721154123128e-15, 0.7596123493895988)
To generate our dataset we need to pass a list of $\theta$ to our robotic arm and save the endeffector's coordinates. So, during the training, our input will be the endeffector's coordinates and our output will be $\theta$ values.
Para gerar nossos dados de treino, passaremos uma série de valores de $\theta$ para nosso manipulador e salvaremos os valores das coordenadas do endeffector. Assim, na hora de treinar, nosso input será as coordenadas do endeffector e nosso output os valores de theta.
from src.data.make_dataset import make_dataset
theta1_zone1 = list(range(0, 100, 10)) + list(range(180, 280, 10))
theta1_zone2 = list(range(90, 190, 10)) + list(range(270, 370, 10))
theta2 = list(range(-90, 100, 10))
theta3 = list(range(-90, 100, 10))
X_zone1, y_zone1 = make_dataset(robot, [theta1_zone1, theta2, theta3], 'data_zone1')
X_zone2, y_zone2 = make_dataset(robot, [theta1_zone2, theta2, theta3], 'data_zone2')
data_for_dataframe = []
for x, y in zip(X_zone1, y_zone1):
x_ = list(x)
x_.extend(list(y))
x_.extend([1])
data_for_dataframe.append(x_)
for x, y in zip(X_zone2, y_zone2):
x_ = list(x)
x_.extend(list(y))
x_.extend([2])
data_for_dataframe.append(x_)
df = pd.DataFrame(data_for_dataframe, columns=['X', 'Y', 'Z', 't1', 't2', 't3', 'zona'])
print('Shape inicial: ', df.shape)
for column in ['X', 'Y', 'Z']:
df[column] = df[column].apply(lambda x: round(x, 4))
df_clean = df.drop_duplicates(subset=['X','Y','Z', 'zona'], keep='first')
print('Shape final: ', df_clean.shape)
Shape inicial: (14440, 7) Shape final: (7020, 7)
df_clean['zona'].value_counts()
2 3510 1 3510 Name: zona, dtype: int64
df_clean.head()
| X | Y | Z | t1 | t2 | t3 | zona | |
|---|---|---|---|---|---|---|---|
| 0 | -50.0000 | -0.0 | 50.0000 | 0.0 | -90.0 | -90.0 | 1 |
| 1 | -49.2404 | -0.0 | 41.3176 | 0.0 | -90.0 | -80.0 | 1 |
| 2 | -46.9846 | -0.0 | 32.8990 | 0.0 | -90.0 | -70.0 | 1 |
| 3 | -43.3013 | -0.0 | 25.0000 | 0.0 | -90.0 | -60.0 | 1 |
| 4 | -38.3022 | -0.0 | 17.8606 | 0.0 | -90.0 | -50.0 | 1 |
df_zona1 = df_clean[df_clean.zona == 1]
df_zona2 = df_clean[df_clean.zona == 2]
df_total = df_clean.drop_duplicates(subset=['X','Y','Z'], keep='first')
X_zone1, y_zone1 = df_zona1[['X', 'Y', 'Z']].values, df_zona1[['t1', 't2', 't3']].values
X_zone2, y_zone2 = df_zona2[['X', 'Y', 'Z']].values, df_zona2[['t1', 't2', 't3']].values
X_total, y_total = df_clean[['X', 'Y', 'Z']].values, df_clean[['t1', 't2', 't3']].values
X_zone2[324], y_zone2[324]
(array([-15.2016, 86.2126, 76.543 ]), array([100., -40., 50.]))
theta1 = list(range(0, 360, 10))
robot.plot_workspace([theta1, theta2, theta3], [90, 0, 0])
initial = [90, 0, 0]
theta1_zone1 = list(range(0, 100, 10))
theta1_zone2 = list(range(180, 280, 10))
theta2 = list(range(-90, 100, 10))
theta3 = list(range(-90, 100, 10))
X, Y, Z = [], [], []
for t1 in theta1_zone1:
for t2 in theta2:
for t3 in theta3:
robot.update_theta(0, t1)
robot.update_theta(1, t2)
robot.update_theta(2, t3)
robot.forward_kinematics()
if robot.get_X() >= 0 and robot.get_Y()>=0:
X.append(robot.get_X())
Y.append(robot.get_Y())
Z.append(robot.get_Z())
for i in range(len(robot.joints)):
robot.update_theta(i, initial[i])
robot.forward_kinematics()
X_ = robot.get_all_X()
Y_ = robot.get_all_Y()
Z_ = robot.get_all_Z()
fig = go.Figure(data=go.Mesh3d(x=X, y=Y, z=Z,
alphahull=5,
opacity=0.5,
color='orange'
))
X, Y, Z = [], [], []
for t1 in theta1_zone2:
for t2 in theta2:
for t3 in theta3:
robot.update_theta(0, t1)
robot.update_theta(1, t2)
robot.update_theta(2, t3)
robot.forward_kinematics()
if round(robot.get_X(),4) <= 0 and round(robot.get_Y(),4) <= 0:
X.append(robot.get_X())
Y.append(robot.get_Y())
Z.append(robot.get_Z())
fig.add_trace(go.Mesh3d(x=X, y=Y, z=Z,
alphahull=5,
opacity=0.5,
color='orange'
))
initial = [90, 0, 0]
theta1_zone1 = list(range(90, 190, 10))
theta1_zone2 = list(range(270, 370, 10))
X, Y, Z = [], [], []
X_Y = []
for t1 in theta1_zone1:
for t2 in theta2:
for t3 in theta3:
robot.update_theta(0, t1)
robot.update_theta(1, t2)
robot.update_theta(2, t3)
robot.forward_kinematics()
if round(robot.get_Y(),4) >= 0 and round(robot.get_X(),4) <= 0:
X.append(robot.get_X())
Y.append(robot.get_Y())
Z.append(robot.get_Z())
for i in range(len(robot.joints)):
robot.update_theta(i, initial[i])
robot.forward_kinematics()
X_ = robot.get_all_X()
Y_ = robot.get_all_Y()
Z_ = robot.get_all_Z()
fig.add_trace(go.Mesh3d(x=X, y=Y, z=Z,
alphahull=5,
opacity=0.6,
color='blue'
))
X, Y, Z = [], [], []
for t1 in theta1_zone2:
for t2 in theta2:
for t3 in theta3:
robot.update_theta(0, t1)
robot.update_theta(1, t2)
robot.update_theta(2, t3)
robot.forward_kinematics()
if round(robot.get_Y(), 4) <= 0 and round(robot.get_X(), 4) >= 0:
X.append(robot.get_X())
Y.append(robot.get_Y())
Z.append(robot.get_Z())
fig.add_trace(go.Mesh3d(x=X, y=Y, z=Z,
alphahull=5,
opacity=0.6,
color='blue'
))
fig.add_trace(go.Scatter3d(
x=X_, y=Y_, z=Z_,
marker=dict(
size=0,
color=3,
colorscale='Viridis',
),
line=dict(
color='darkblue',
width=5
)
))
fig.update_layout(
width=700, height=700, autosize=True, scene_aspectmode='cube', showlegend=False)
fig.show()
from sklearn.preprocessing import StandardScaler
indices = np.arange(X_zone1.shape[0])
np.random.shuffle(indices)
X_zone1 = X_zone1[indices]
y_zone1 = y_zone1[indices]
indices = np.arange(X_zone2.shape[0])
np.random.shuffle(indices)
X_zone2 = X_zone2[indices]
y_zone2 = y_zone2[indices]
indices = np.arange(X_total.shape[0])
np.random.shuffle(indices)
X_total = X_total[indices]
y_total = y_total[indices]
scaler_X1 = StandardScaler()
X_zone1 = scaler_X1.fit_transform(X_zone1)
scaler_X2 = StandardScaler()
X_zone2 = scaler_X2.fit_transform(X_zone2)
scaler_Xtotal = StandardScaler()
X_total = scaler_Xtotal.fit_transform(X_total)
test_set_input1 = []
test_set_output1 = []
test_set_input2 = []
test_set_output2 = []
X, Y, Z = [], [], []
input_pred = []
final_X, final_Y, final_Z = [], [], []
points = []
robot.update_theta(0, 0)
robot.update_theta(1, 0)
robot.update_theta(2, -90)
theta2 = 0
theta3 = -90
for k in range(3):
for i in range(0, 360, 15):
robot.update_theta(0, i)
robot.update_theta(1, theta2)
robot.update_theta(2, theta3)
robot.forward_kinematics()
X = robot.get_all_X()
Y = robot.get_all_Y()
Z = robot.get_all_Z()
points.append([X, Y, Z])
input_pred.append([robot.get_X(), robot.get_Y(), robot.get_Z()])
final_X.append(robot.get_X())
final_Y.append(robot.get_Y())
final_Z.append(robot.get_Z())
if (robot.get_X() >= 0 and robot.get_Y() >= 0) or (robot.get_X() < 0 and robot.get_Y() < 0):
test_set_input1.append(list(scaler_X1.transform([[robot.get_X(), robot.get_Y(), robot.get_Z()]])[0]))
test_set_output1.append([i, theta2, theta3])
else:
test_set_input2.append(list(scaler_X2.transform([[robot.get_X(), robot.get_Y(), robot.get_Z()]])[0]))
test_set_output2.append([i, theta2, theta3])
theta2 += 1
theta3 += 1
X_2, Y_2, Z_2 = [], [], []
input_pred_2 = []
final_X_2, final_Y_2, final_Z_2 = [], [], []
points_2 = []
theta2 = 0
theta3 = -90
robot.update_theta(0, 100)
robot.update_theta(1, theta2)
robot.update_theta(2, theta3)
for i in range(-90, 90, 10):
robot.update_theta(2, i)
robot.forward_kinematics()
X = robot.get_all_X()
Y = robot.get_all_Y()
Z = robot.get_all_Z()
points_2.append([X, Y, Z])
input_pred_2.append([robot.get_X(), robot.get_Y(), robot.get_Z()])
final_X_2.append(robot.get_X())
final_Y_2.append(robot.get_Y())
final_Z_2.append(robot.get_Z())
if (robot.get_X() >= 0 and robot.get_Y() >= 0) or (robot.get_X() < 0 and robot.get_Y() < 0):
test_set_input1.append(list(scaler_X1.transform([[robot.get_X(), robot.get_Y(), robot.get_Z()]])[0]))
test_set_output1.append([100, theta2, i])
else:
test_set_input2.append(list(scaler_X2.transform([[robot.get_X(), robot.get_Y(), robot.get_Z()]])[0]))
test_set_output2.append([100, theta2, i])
fig = go.Figure(data=go.Scatter3d(
x=final_X, y=final_Y, z=final_Z,
marker=dict(
size=0,
color=3,
colorscale='Viridis',
),
line=dict(
color='darkblue',
width=2
)
))
fig.update_layout(scene = dict(
xaxis = dict(nticks=8, range=[min(final_X) - 1,max(final_X) + 1]),
yaxis = dict(nticks=8, range=[min(final_Y) - 1,max(final_Y) + 1]),
zaxis = dict(nticks=8, range=[min(final_Z) - 1,max(final_Z) + 1])),
width=700, height=700)
fig.show()
fig = go.Figure(data=go.Scatter3d(
x=final_X_2, y=final_Y_2, z=final_Z_2,
marker=dict(
size=0,
color=3,
colorscale='Viridis',
),
line=dict(
color='darkblue',
width=2
)
))
fig.update_layout(scene = dict(
xaxis = dict(nticks=8, range=[min(final_X_2) - 1,max(final_X_2) + 1]),
yaxis = dict(nticks=8, range=[min(final_Y_2) - 1,max(final_Y_2) + 1]),
zaxis = dict(nticks=8, range=[min(final_Z_2) - 1,max(final_Z_2) + 1])),
width=700, height=700)
fig.show()
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers import Dense, Activation, LeakyReLU, Input, Lambda, Concatenate, Dropout
from sklearn.metrics import mean_absolute_error
class MAEHistory(tf.keras.callbacks.Callback):
def __init__(self, train=None, robot=None, scaler=None, validation=None):
super(MAEHistory, self).__init__()
self.validation = validation
self.train = train
self.robot = robot
self.scaler = scaler
def on_epoch_end(self, epoch, logs={}):
logs['MAE_score_train'] = float('-inf')
X_train, y_train = self.train[0], self.train[1]
y_pred = (self.model.predict(X_train))
points_final = []
for theta1, theta2, theta3 in y_pred:
self.robot.update_theta(0, theta1)
self.robot.update_theta(1, theta2)
self.robot.update_theta(2, theta3)
self.robot.forward_kinematics()
points_final.append([robot.get_X(), robot.get_Y(), robot.get_Z()])
score = mean_absolute_error(points_final, self.scaler.inverse_transform(X_train))
if (self.validation):
logs['MAE_score_val'] = float('-inf')
X_valid, y_valid = self.validation[0], self.validation[1]
y_val_pred = (self.model.predict(X_valid))
points_final = []
for theta1, theta2, theta3 in y_val_pred:
self.robot.update_theta(0, theta1)
self.robot.update_theta(1, theta2)
self.robot.update_theta(2, theta3)
self.robot.forward_kinematics()
points_final.append([robot.get_X(), robot.get_Y(), robot.get_Z()])
val_score = mean_absolute_error(points_final, self.scaler.inverse_transform(X_valid))
logs['MAE_score_train'] = np.round(score, 5)
logs['MAE_score_val'] = np.round(val_score, 5)
print("MAE train:", np.round(score, 5))
print("MAE test:", np.round(val_score, 5))
else:
logs['MAE_score_train'] = np.round(score, 5)
model1 = keras.Sequential([
Dense(256, input_shape=(3,)),
LeakyReLU(),
Dropout(0.2),
Dense(256),
LeakyReLU(),
Dropout(0.2),
Dense(256),
LeakyReLU(),
Dropout(0.2),
Dense(256),
LeakyReLU(),
Dropout(0.2),
Dense(256),
LeakyReLU(),
Dropout(0.2),
Dense(256),
LeakyReLU(),
Dropout(0.2),
Dense(256),
LeakyReLU(),
Dense(3)
])
model2 = keras.Sequential([
Dense(256, input_shape=(3,)),
LeakyReLU(),
Dropout(0.2),
Dense(256),
LeakyReLU(),
Dropout(0.2),
Dense(256),
LeakyReLU(),
Dropout(0.2),
Dense(256),
LeakyReLU(),
Dropout(0.2),
Dense(256),
LeakyReLU(),
Dropout(0.2),
Dense(256),
LeakyReLU(),
Dropout(0.2),
Dense(256),
LeakyReLU(),
Dense(3)
])
model_total = keras.Sequential([
Dense(256, input_shape=(3,)),
LeakyReLU(),
Dropout(0.2),
Dense(256),
LeakyReLU(),
Dropout(0.2),
Dense(256),
LeakyReLU(),
Dropout(0.2),
Dense(256),
LeakyReLU(),
Dropout(0.2),
Dense(256),
LeakyReLU(),
Dropout(0.2),
Dense(256),
LeakyReLU(),
Dropout(0.2),
Dense(256),
LeakyReLU(),
Dense(3)
])
learning_rate_fn = keras.optimizers.schedules.InverseTimeDecay(
1e-2, 250, 0.4)
model1.compile(optimizer = keras.optimizers.Adam(learning_rate=learning_rate_fn), loss='mse')
model2.compile(optimizer = keras.optimizers.Adam(learning_rate=learning_rate_fn), loss='mse')
model_total.compile(optimizer = keras.optimizers.Adam(learning_rate=learning_rate_fn), loss='mse')
model1.summary(), model2.summary(), model_total.summary()
Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense (Dense) (None, 256) 1024 _________________________________________________________________ leaky_re_lu (LeakyReLU) (None, 256) 0 _________________________________________________________________ dropout (Dropout) (None, 256) 0 _________________________________________________________________ dense_1 (Dense) (None, 256) 65792 _________________________________________________________________ leaky_re_lu_1 (LeakyReLU) (None, 256) 0 _________________________________________________________________ dropout_1 (Dropout) (None, 256) 0 _________________________________________________________________ dense_2 (Dense) (None, 256) 65792 _________________________________________________________________ leaky_re_lu_2 (LeakyReLU) (None, 256) 0 _________________________________________________________________ dropout_2 (Dropout) (None, 256) 0 _________________________________________________________________ dense_3 (Dense) (None, 256) 65792 _________________________________________________________________ leaky_re_lu_3 (LeakyReLU) (None, 256) 0 _________________________________________________________________ dropout_3 (Dropout) (None, 256) 0 _________________________________________________________________ dense_4 (Dense) (None, 256) 65792 _________________________________________________________________ leaky_re_lu_4 (LeakyReLU) (None, 256) 0 _________________________________________________________________ dropout_4 (Dropout) (None, 256) 0 _________________________________________________________________ dense_5 (Dense) (None, 256) 65792 _________________________________________________________________ leaky_re_lu_5 (LeakyReLU) (None, 256) 0 _________________________________________________________________ dropout_5 (Dropout) (None, 256) 0 _________________________________________________________________ dense_6 (Dense) (None, 256) 65792 _________________________________________________________________ leaky_re_lu_6 (LeakyReLU) (None, 256) 0 _________________________________________________________________ dense_7 (Dense) (None, 3) 771 ================================================================= Total params: 396,547 Trainable params: 396,547 Non-trainable params: 0 _________________________________________________________________ Model: "sequential_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_8 (Dense) (None, 256) 1024 _________________________________________________________________ leaky_re_lu_7 (LeakyReLU) (None, 256) 0 _________________________________________________________________ dropout_6 (Dropout) (None, 256) 0 _________________________________________________________________ dense_9 (Dense) (None, 256) 65792 _________________________________________________________________ leaky_re_lu_8 (LeakyReLU) (None, 256) 0 _________________________________________________________________ dropout_7 (Dropout) (None, 256) 0 _________________________________________________________________ dense_10 (Dense) (None, 256) 65792 _________________________________________________________________ leaky_re_lu_9 (LeakyReLU) (None, 256) 0 _________________________________________________________________ dropout_8 (Dropout) (None, 256) 0 _________________________________________________________________ dense_11 (Dense) (None, 256) 65792 _________________________________________________________________ leaky_re_lu_10 (LeakyReLU) (None, 256) 0 _________________________________________________________________ dropout_9 (Dropout) (None, 256) 0 _________________________________________________________________ dense_12 (Dense) (None, 256) 65792 _________________________________________________________________ leaky_re_lu_11 (LeakyReLU) (None, 256) 0 _________________________________________________________________ dropout_10 (Dropout) (None, 256) 0 _________________________________________________________________ dense_13 (Dense) (None, 256) 65792 _________________________________________________________________ leaky_re_lu_12 (LeakyReLU) (None, 256) 0 _________________________________________________________________ dropout_11 (Dropout) (None, 256) 0 _________________________________________________________________ dense_14 (Dense) (None, 256) 65792 _________________________________________________________________ leaky_re_lu_13 (LeakyReLU) (None, 256) 0 _________________________________________________________________ dense_15 (Dense) (None, 3) 771 ================================================================= Total params: 396,547 Trainable params: 396,547 Non-trainable params: 0 _________________________________________________________________ Model: "sequential_2" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_16 (Dense) (None, 256) 1024 _________________________________________________________________ leaky_re_lu_14 (LeakyReLU) (None, 256) 0 _________________________________________________________________ dropout_12 (Dropout) (None, 256) 0 _________________________________________________________________ dense_17 (Dense) (None, 256) 65792 _________________________________________________________________ leaky_re_lu_15 (LeakyReLU) (None, 256) 0 _________________________________________________________________ dropout_13 (Dropout) (None, 256) 0 _________________________________________________________________ dense_18 (Dense) (None, 256) 65792 _________________________________________________________________ leaky_re_lu_16 (LeakyReLU) (None, 256) 0 _________________________________________________________________ dropout_14 (Dropout) (None, 256) 0 _________________________________________________________________ dense_19 (Dense) (None, 256) 65792 _________________________________________________________________ leaky_re_lu_17 (LeakyReLU) (None, 256) 0 _________________________________________________________________ dropout_15 (Dropout) (None, 256) 0 _________________________________________________________________ dense_20 (Dense) (None, 256) 65792 _________________________________________________________________ leaky_re_lu_18 (LeakyReLU) (None, 256) 0 _________________________________________________________________ dropout_16 (Dropout) (None, 256) 0 _________________________________________________________________ dense_21 (Dense) (None, 256) 65792 _________________________________________________________________ leaky_re_lu_19 (LeakyReLU) (None, 256) 0 _________________________________________________________________ dropout_17 (Dropout) (None, 256) 0 _________________________________________________________________ dense_22 (Dense) (None, 256) 65792 _________________________________________________________________ leaky_re_lu_20 (LeakyReLU) (None, 256) 0 _________________________________________________________________ dense_23 (Dense) (None, 3) 771 ================================================================= Total params: 396,547 Trainable params: 396,547 Non-trainable params: 0 _________________________________________________________________
(None, None, None)
history = model1.fit(X_zone1, y_zone1, epochs=1000, batch_size=32, workers=-1, use_multiprocessing=True,
callbacks=[MAEHistory(train=(X_zone1, y_zone1), robot=robot, scaler=scaler_X1, validation=(test_set_input1, test_set_output1))])
Epoch 1/1000 110/110 [==============================] - 1s 5ms/step - loss: 3949.8032 MAE train: 36.10208 MAE test: 27.24905 Epoch 2/1000 110/110 [==============================] - 1s 5ms/step - loss: 1950.8214 MAE train: 17.8976 MAE test: 17.15468 Epoch 3/1000 110/110 [==============================] - 1s 5ms/step - loss: 1516.8636 MAE train: 17.11547 MAE test: 18.1822 Epoch 4/1000 110/110 [==============================] - 1s 5ms/step - loss: 1323.0868 MAE train: 17.05132 MAE test: 16.95209 Epoch 5/1000 110/110 [==============================] - 1s 5ms/step - loss: 1335.6745 MAE train: 19.38544 MAE test: 18.60112 Epoch 6/1000 110/110 [==============================] - 1s 6ms/step - loss: 1308.0861 MAE train: 15.96718 MAE test: 15.35551 Epoch 7/1000 110/110 [==============================] - 1s 5ms/step - loss: 1154.5553 MAE train: 15.7554 MAE test: 13.03972 Epoch 8/1000 110/110 [==============================] - 1s 6ms/step - loss: 1162.2901 MAE train: 19.29107 MAE test: 20.91065 Epoch 9/1000 110/110 [==============================] - 1s 6ms/step - loss: 1003.0952 MAE train: 14.37731 MAE test: 16.88645 Epoch 10/1000 110/110 [==============================] - 1s 5ms/step - loss: 1051.5751 MAE train: 10.59607 MAE test: 8.15373 Epoch 11/1000 110/110 [==============================] - 1s 6ms/step - loss: 965.4058 MAE train: 10.05264 MAE test: 8.09305 Epoch 12/1000 110/110 [==============================] - 1s 8ms/step - loss: 980.6033 MAE train: 12.1953 MAE test: 14.32901 Epoch 13/1000 110/110 [==============================] - 1s 6ms/step - loss: 907.1737 MAE train: 10.17435 MAE test: 9.35467 Epoch 14/1000 110/110 [==============================] - 1s 5ms/step - loss: 829.7878 MAE train: 12.65369 MAE test: 12.73521 Epoch 15/1000 110/110 [==============================] - 1s 5ms/step - loss: 781.2813 MAE train: 9.41458 MAE test: 10.14349 Epoch 16/1000 110/110 [==============================] - 1s 6ms/step - loss: 793.5264 MAE train: 10.04662 MAE test: 8.02455 Epoch 17/1000 110/110 [==============================] - 1s 5ms/step - 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loss: 126.4294 MAE train: 3.30557 MAE test: 3.38565 Epoch 901/1000 110/110 [==============================] - 1s 6ms/step - loss: 127.2213 MAE train: 3.71136 MAE test: 4.31339 Epoch 902/1000 110/110 [==============================] - 1s 6ms/step - loss: 137.1449 MAE train: 3.27219 MAE test: 3.34513 Epoch 903/1000 110/110 [==============================] - 1s 6ms/step - loss: 142.0704 MAE train: 3.83371 MAE test: 4.29641 Epoch 904/1000 110/110 [==============================] - 1s 6ms/step - loss: 134.7305 MAE train: 3.43632 MAE test: 3.61407 Epoch 905/1000 110/110 [==============================] - 1s 6ms/step - loss: 143.5934 MAE train: 3.39673 MAE test: 3.49731 Epoch 906/1000 110/110 [==============================] - 1s 6ms/step - loss: 145.3424 MAE train: 3.56616 MAE test: 3.69701 Epoch 907/1000 110/110 [==============================] - 1s 6ms/step - loss: 131.0173 MAE train: 3.49367 MAE test: 3.84007 Epoch 908/1000 110/110 [==============================] - 1s 6ms/step - loss: 119.1408 MAE train: 3.62264 MAE test: 4.05283 Epoch 909/1000 110/110 [==============================] - 1s 5ms/step - loss: 122.3277 MAE train: 3.34109 MAE test: 3.32143 Epoch 910/1000 110/110 [==============================] - 1s 5ms/step - loss: 145.4218 MAE train: 3.99755 MAE test: 3.75477 Epoch 911/1000 110/110 [==============================] - 1s 5ms/step - loss: 127.8916 MAE train: 4.06578 MAE test: 4.59282 Epoch 912/1000 110/110 [==============================] - 1s 6ms/step - loss: 130.7670 MAE train: 3.14711 MAE test: 2.95176 Epoch 913/1000 110/110 [==============================] - 1s 6ms/step - loss: 134.1721 MAE train: 3.73655 MAE test: 4.20453 Epoch 914/1000 110/110 [==============================] - 1s 6ms/step - loss: 125.7645 MAE train: 3.71767 MAE test: 3.99406 Epoch 915/1000 110/110 [==============================] - 1s 6ms/step - loss: 131.1550 MAE train: 3.91837 MAE test: 4.53384 Epoch 916/1000 110/110 [==============================] - 1s 6ms/step - loss: 134.6763 MAE train: 3.26411 MAE test: 3.36216 Epoch 917/1000 110/110 [==============================] - 1s 12ms/step - loss: 150.2066 MAE train: 4.0067 MAE test: 4.34678 Epoch 918/1000 110/110 [==============================] - 1s 8ms/step - loss: 131.0484 MAE train: 3.48564 MAE test: 3.8127 Epoch 919/1000 110/110 [==============================] - 1s 6ms/step - loss: 127.9019 MAE train: 3.90358 MAE test: 4.33876 Epoch 920/1000 110/110 [==============================] - 1s 10ms/step - loss: 124.1098 MAE train: 3.79824 MAE test: 4.15259 Epoch 921/1000 110/110 [==============================] - 1s 9ms/step - loss: 133.5223 MAE train: 3.31414 MAE test: 3.67473 Epoch 922/1000 110/110 [==============================] - 1s 8ms/step - loss: 135.7677 MAE train: 3.4073 MAE test: 3.48887 Epoch 923/1000 110/110 [==============================] - 1s 8ms/step - loss: 130.0885 MAE train: 3.62923 MAE test: 4.35185 Epoch 924/1000 110/110 [==============================] - 1s 6ms/step - loss: 123.7804 MAE train: 4.293 MAE test: 4.79817 Epoch 925/1000 110/110 [==============================] - 1s 8ms/step - loss: 153.9615 MAE train: 4.10884 MAE test: 4.65448 Epoch 926/1000 110/110 [==============================] - 1s 6ms/step - loss: 136.9196 MAE train: 3.49573 MAE test: 3.60057 Epoch 927/1000 110/110 [==============================] - 1s 6ms/step - loss: 132.0541 MAE train: 3.76676 MAE test: 4.02237 Epoch 928/1000 110/110 [==============================] - 1s 8ms/step - loss: 146.8161 MAE train: 3.81531 MAE test: 3.99957 Epoch 929/1000 110/110 [==============================] - 1s 7ms/step - loss: 136.4678 MAE train: 3.71945 MAE test: 4.09006 Epoch 930/1000 110/110 [==============================] - 1s 7ms/step - loss: 129.5739 MAE train: 4.04959 MAE test: 4.45763 Epoch 931/1000 110/110 [==============================] - 1s 7ms/step - loss: 131.9406 MAE train: 3.56815 MAE test: 3.83219 Epoch 932/1000 110/110 [==============================] - 1s 7ms/step - loss: 124.3401 MAE train: 3.52441 MAE test: 3.57179 Epoch 933/1000 110/110 [==============================] - 1s 7ms/step - loss: 133.6479 MAE train: 3.59911 MAE test: 4.14603 Epoch 934/1000 110/110 [==============================] - 1s 7ms/step - loss: 131.0289 MAE train: 3.73057 MAE test: 4.2053 Epoch 935/1000 110/110 [==============================] - 1s 7ms/step - loss: 127.5494 MAE train: 3.57156 MAE test: 3.7542 Epoch 936/1000 110/110 [==============================] - 1s 7ms/step - loss: 140.9657 MAE train: 3.94963 MAE test: 4.29065 Epoch 937/1000 110/110 [==============================] - 1s 6ms/step - loss: 142.9120 MAE train: 3.62573 MAE test: 3.87687 Epoch 938/1000 110/110 [==============================] - 1s 6ms/step - loss: 123.1617 MAE train: 3.99822 MAE test: 4.42211 Epoch 939/1000 110/110 [==============================] - 1s 6ms/step - loss: 154.4557 MAE train: 3.76502 MAE test: 4.56582 Epoch 940/1000 110/110 [==============================] - 1s 6ms/step - loss: 131.6360 MAE train: 4.17629 MAE test: 4.89288 Epoch 941/1000 110/110 [==============================] - 1s 6ms/step - loss: 124.8347 MAE train: 3.58588 MAE test: 4.08029 Epoch 942/1000 110/110 [==============================] - 1s 6ms/step - loss: 118.9548 MAE train: 3.34759 MAE test: 3.57936 Epoch 943/1000 110/110 [==============================] - 1s 6ms/step - loss: 131.6340 MAE train: 3.51478 MAE test: 3.60976 Epoch 944/1000 110/110 [==============================] - 1s 6ms/step - loss: 128.1397 MAE train: 3.68416 MAE test: 4.0846 Epoch 945/1000 110/110 [==============================] - 1s 7ms/step - loss: 117.9016 MAE train: 3.80056 MAE test: 4.23118 Epoch 946/1000 110/110 [==============================] - 1s 6ms/step - loss: 134.4632 MAE train: 3.80832 MAE test: 4.30394 Epoch 947/1000 110/110 [==============================] - 1s 6ms/step - loss: 118.1199 MAE train: 4.29518 MAE test: 4.78979 Epoch 948/1000 110/110 [==============================] - 1s 6ms/step - loss: 127.2839 MAE train: 3.2536 MAE test: 3.21942 Epoch 949/1000 110/110 [==============================] - 1s 7ms/step - loss: 123.9413 MAE train: 3.90483 MAE test: 4.62636 Epoch 950/1000 110/110 [==============================] - 1s 6ms/step - loss: 134.3548 MAE train: 3.19485 MAE test: 3.3002 Epoch 951/1000 110/110 [==============================] - 1s 7ms/step - loss: 121.8931 MAE train: 3.87366 MAE test: 4.51057 Epoch 952/1000 110/110 [==============================] - 1s 6ms/step - loss: 147.0773 MAE train: 3.6044 MAE test: 3.83022 Epoch 953/1000 110/110 [==============================] - 1s 6ms/step - loss: 126.1784 MAE train: 3.40127 MAE test: 3.66869 Epoch 954/1000 110/110 [==============================] - 1s 6ms/step - loss: 156.4655 MAE train: 3.83945 MAE test: 4.20886 Epoch 955/1000 110/110 [==============================] - 1s 6ms/step - loss: 137.4821 MAE train: 4.06502 MAE test: 4.27154 Epoch 956/1000 110/110 [==============================] - 1s 7ms/step - loss: 110.7633 MAE train: 3.42879 MAE test: 3.79085 Epoch 957/1000 110/110 [==============================] - 1s 6ms/step - loss: 114.3640 MAE train: 3.78822 MAE test: 3.8992 Epoch 958/1000 110/110 [==============================] - 1s 8ms/step - loss: 140.4290 MAE train: 3.9924 MAE test: 4.46738 Epoch 959/1000 110/110 [==============================] - 1s 6ms/step - loss: 125.0174 MAE train: 3.41942 MAE test: 3.72624 Epoch 960/1000 110/110 [==============================] - 1s 6ms/step - loss: 115.7425 MAE train: 4.08912 MAE test: 4.51636 Epoch 961/1000 110/110 [==============================] - 1s 7ms/step - loss: 158.2333A: 0s - loss: 161 MAE train: 3.8418 MAE test: 4.40225 Epoch 962/1000 110/110 [==============================] - 1s 6ms/step - loss: 122.6379 MAE train: 3.4634 MAE test: 4.02392 Epoch 963/1000 110/110 [==============================] - 1s 6ms/step - loss: 129.4642 MAE train: 3.69123 MAE test: 4.2783 Epoch 964/1000 110/110 [==============================] - 1s 6ms/step - loss: 139.0770 MAE train: 3.97874 MAE test: 4.84651 Epoch 965/1000 110/110 [==============================] - 1s 6ms/step - loss: 122.8328 MAE train: 3.79804 MAE test: 4.29601 Epoch 966/1000 110/110 [==============================] - 1s 7ms/step - loss: 144.7548 MAE train: 3.51834 MAE test: 3.55615 Epoch 967/1000 110/110 [==============================] - 1s 6ms/step - loss: 146.9288 MAE train: 3.59149 MAE test: 3.7689 Epoch 968/1000 110/110 [==============================] - 1s 6ms/step - loss: 123.6866 MAE train: 3.78766 MAE test: 4.07829 Epoch 969/1000 110/110 [==============================] - 1s 6ms/step - loss: 126.4026 MAE train: 3.59253 MAE test: 4.06661 Epoch 970/1000 110/110 [==============================] - 1s 7ms/step - loss: 140.5466 MAE train: 3.84812 MAE test: 4.23816 Epoch 971/1000 110/110 [==============================] - 1s 6ms/step - loss: 152.1328 MAE train: 3.69809 MAE test: 3.90859 Epoch 972/1000 110/110 [==============================] - 1s 6ms/step - loss: 130.9823 MAE train: 3.4954 MAE test: 3.86997 Epoch 973/1000 110/110 [==============================] - 1s 7ms/step - loss: 127.3949 MAE train: 3.74704 MAE test: 4.03148 Epoch 974/1000 110/110 [==============================] - 1s 7ms/step - loss: 128.7605 MAE train: 4.15439 MAE test: 4.67919 Epoch 975/1000 110/110 [==============================] - 1s 6ms/step - loss: 121.2588 MAE train: 4.04242 MAE test: 4.59804 Epoch 976/1000 110/110 [==============================] - 1s 6ms/step - loss: 136.6685 MAE train: 3.66559 MAE test: 4.03109 Epoch 977/1000 110/110 [==============================] - 1s 6ms/step - loss: 138.7458 MAE train: 3.86316 MAE test: 4.63233 Epoch 978/1000 110/110 [==============================] - 1s 6ms/step - loss: 124.5979 MAE train: 3.76593 MAE test: 4.34467 Epoch 979/1000 110/110 [==============================] - 1s 6ms/step - loss: 127.8597 MAE train: 4.38322 MAE test: 5.00243 Epoch 980/1000 110/110 [==============================] - 1s 6ms/step - loss: 121.2995 MAE train: 3.73259 MAE test: 4.14399 Epoch 981/1000 110/110 [==============================] - 1s 6ms/step - loss: 139.9875 MAE train: 3.41869 MAE test: 3.64507 Epoch 982/1000 110/110 [==============================] - 1s 6ms/step - loss: 126.6571 MAE train: 3.42452 MAE test: 3.74936 Epoch 983/1000 110/110 [==============================] - 1s 6ms/step - loss: 121.5054 MAE train: 3.92261 MAE test: 3.99401 Epoch 984/1000 110/110 [==============================] - 1s 6ms/step - loss: 120.3898 MAE train: 3.60462 MAE test: 4.10513 Epoch 985/1000 110/110 [==============================] - 1s 6ms/step - loss: 121.6939 MAE train: 4.02987 MAE test: 4.37715 Epoch 986/1000 110/110 [==============================] - 1s 6ms/step - loss: 116.9795 MAE train: 3.66509 MAE test: 4.11252 Epoch 987/1000 110/110 [==============================] - 1s 5ms/step - loss: 130.2659 MAE train: 3.50716 MAE test: 3.84055 Epoch 988/1000 110/110 [==============================] - 1s 6ms/step - loss: 139.1597 MAE train: 3.53525 MAE test: 3.88285 Epoch 989/1000 110/110 [==============================] - 1s 6ms/step - loss: 123.6028 MAE train: 3.7794 MAE test: 4.04149 Epoch 990/1000 110/110 [==============================] - 1s 5ms/step - loss: 134.3452 MAE train: 3.50129 MAE test: 3.93815 Epoch 991/1000 110/110 [==============================] - 1s 6ms/step - loss: 127.3713 MAE train: 3.25479 MAE test: 3.46231 Epoch 992/1000 110/110 [==============================] - 1s 6ms/step - loss: 139.9845 MAE train: 4.00468 MAE test: 4.41841 Epoch 993/1000 110/110 [==============================] - 1s 5ms/step - loss: 123.1551 MAE train: 4.30889 MAE test: 5.04331 Epoch 994/1000 110/110 [==============================] - ETA: 0s - loss: 137.609 - 1s 5ms/step - loss: 137.2851 MAE train: 4.19071 MAE test: 5.09945 Epoch 995/1000 110/110 [==============================] - 1s 5ms/step - loss: 126.8474 MAE train: 3.67575 MAE test: 4.20692 Epoch 996/1000 110/110 [==============================] - 1s 6ms/step - loss: 125.6914 MAE train: 3.76515 MAE test: 4.22708 Epoch 997/1000 110/110 [==============================] - 1s 6ms/step - loss: 125.6784 MAE train: 3.72508 MAE test: 4.04456 Epoch 998/1000 110/110 [==============================] - 1s 6ms/step - loss: 125.7932 MAE train: 3.34682 MAE test: 3.29988 Epoch 999/1000 110/110 [==============================] - 1s 6ms/step - loss: 150.7040A: 0s - loss: 16 MAE train: 3.2924 MAE test: 3.7707 Epoch 1000/1000 110/110 [==============================] - 1s 6ms/step - loss: 119.1017 MAE train: 3.72366 MAE test: 4.32704
plt.figure(figsize=(20,20))
plt.plot(history.history['MAE_score_train'])
plt.plot(history.history['MAE_score_val'])
plt.ylabel('MAE', fontsize=30)
plt.xlabel('Epoch', fontsize=30)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.legend(['Treino', 'Teste'], loc='upper right', fontsize=25)
plt.savefig('model_zone_1.png')
plt.show()
history_2 = model2.fit(X_zone2, y_zone2, epochs=1000, batch_size=32, workers=-1, use_multiprocessing=True,
callbacks=[MAEHistory(train=(X_zone2, y_zone2), robot=robot, scaler=scaler_X2, validation=(test_set_input2, test_set_output2))])
Epoch 1/1000 110/110 [==============================] - 1s 5ms/step - loss: 40259.3480 MAE train: 24.67969 MAE test: 21.74377 Epoch 2/1000 110/110 [==============================] - 1s 5ms/step - loss: 1862.4278 MAE train: 17.26101 MAE test: 13.99047 Epoch 3/1000 110/110 [==============================] - 1s 5ms/step - loss: 1469.8862 MAE train: 16.68471 MAE test: 15.70549 Epoch 4/1000 110/110 [==============================] - 1s 5ms/step - loss: 1308.8120 MAE train: 13.87328 MAE test: 10.98917 Epoch 5/1000 110/110 [==============================] - 1s 6ms/step - loss: 1214.4814 MAE train: 12.99961 MAE test: 11.14344 Epoch 6/1000 110/110 [==============================] - 1s 5ms/step - loss: 1087.5171 MAE train: 11.19147 MAE test: 10.6659 Epoch 7/1000 110/110 [==============================] - 1s 6ms/step - loss: 1064.4121 MAE train: 10.40845 MAE test: 8.91049 Epoch 8/1000 110/110 [==============================] - 1s 5ms/step - loss: 966.0771 MAE train: 13.46505 MAE test: 13.34855 Epoch 9/1000 110/110 [==============================] - 1s 6ms/step - loss: 993.4893 MAE train: 11.04311 MAE test: 9.37576 Epoch 10/1000 110/110 [==============================] - 1s 5ms/step - loss: 925.4844 MAE train: 13.79823 MAE test: 15.78925 Epoch 11/1000 110/110 [==============================] - 1s 8ms/step - loss: 930.7230 MAE train: 12.2179 MAE test: 11.82152 Epoch 12/1000 110/110 [==============================] - 1s 7ms/step - loss: 940.5966 MAE train: 9.56534 MAE test: 9.60983 Epoch 13/1000 110/110 [==============================] - 1s 7ms/step - loss: 854.0001 MAE train: 10.19736 MAE test: 10.52602 Epoch 14/1000 110/110 [==============================] - 1s 7ms/step - loss: 801.4748 MAE train: 9.40292 MAE test: 8.12185 Epoch 15/1000 110/110 [==============================] - 1s 5ms/step - loss: 884.1309 MAE train: 9.05442 MAE test: 7.98114 Epoch 16/1000 110/110 [==============================] - 1s 6ms/step - loss: 801.1716 MAE train: 8.443 MAE test: 8.05757 Epoch 17/1000 110/110 [==============================] - 1s 6ms/step - loss: 894.0979 MAE train: 10.25948 MAE test: 7.32681 Epoch 18/1000 110/110 [==============================] - 1s 5ms/step - loss: 810.8355 MAE train: 8.63038 MAE test: 7.63201 Epoch 19/1000 110/110 [==============================] - 1s 5ms/step - loss: 780.2127 MAE train: 7.92952 MAE test: 7.05538 Epoch 20/1000 110/110 [==============================] - 1s 6ms/step - loss: 801.9535 MAE train: 8.30524 MAE test: 7.70427 Epoch 21/1000 110/110 [==============================] - 1s 5ms/step - loss: 771.2941 MAE train: 7.77313 MAE test: 6.75685 Epoch 22/1000 110/110 [==============================] - 1s 5ms/step - loss: 760.5414 MAE train: 9.72757 MAE test: 8.37266 Epoch 23/1000 110/110 [==============================] - 1s 5ms/step - loss: 740.3182 MAE train: 7.85958 MAE test: 7.4929 Epoch 24/1000 110/110 [==============================] - 1s 6ms/step - loss: 742.4161 MAE train: 7.46698 MAE test: 8.13309 Epoch 25/1000 110/110 [==============================] - 1s 5ms/step - loss: 698.9061 MAE train: 7.68579 MAE test: 7.5619 Epoch 26/1000 110/110 [==============================] - 1s 6ms/step - loss: 719.4076 MAE train: 12.48996 MAE test: 11.12498 Epoch 27/1000 110/110 [==============================] - 1s 6ms/step - loss: 758.2519 MAE train: 9.23314 MAE test: 8.18751 Epoch 28/1000 110/110 [==============================] - 1s 6ms/step - loss: 725.5512 MAE train: 7.42239 MAE test: 6.71694 Epoch 29/1000 110/110 [==============================] - 1s 6ms/step - loss: 691.9630 MAE train: 11.60874 MAE test: 10.03393 Epoch 30/1000 110/110 [==============================] - 1s 6ms/step - loss: 705.0772 MAE train: 7.91951 MAE test: 5.923 Epoch 31/1000 110/110 [==============================] - 1s 6ms/step - loss: 740.2269 MAE train: 7.73254 MAE test: 7.43524 Epoch 32/1000 110/110 [==============================] - 1s 6ms/step - loss: 681.5928 MAE train: 6.97473 MAE test: 5.7946 Epoch 33/1000 110/110 [==============================] - 1s 6ms/step - loss: 685.9137 MAE train: 6.76145 MAE test: 4.70781 Epoch 34/1000 110/110 [==============================] - 1s 6ms/step - loss: 646.7235 MAE train: 7.26506 MAE test: 6.0412 Epoch 35/1000 110/110 [==============================] - 1s 6ms/step - loss: 619.8210 MAE train: 7.85916 MAE test: 6.26444 Epoch 36/1000 110/110 [==============================] - 1s 6ms/step - loss: 642.6917A: 0s - loss: 63 MAE train: 7.42736 MAE test: 5.88876 Epoch 37/1000 110/110 [==============================] - 1s 5ms/step - loss: 649.5446 MAE train: 6.97976 MAE test: 6.16753 Epoch 38/1000 110/110 [==============================] - 1s 5ms/step - loss: 627.6297 MAE train: 7.51263 MAE test: 6.30977 Epoch 39/1000 110/110 [==============================] - 1s 5ms/step - loss: 701.2444 MAE train: 7.13603 MAE test: 5.33214 Epoch 40/1000 110/110 [==============================] - 1s 6ms/step - loss: 738.8928 MAE train: 9.04724 MAE test: 7.30447 Epoch 41/1000 110/110 [==============================] - 1s 6ms/step - loss: 629.9143 MAE train: 6.56598 MAE test: 5.83672 Epoch 42/1000 110/110 [==============================] - 1s 6ms/step - loss: 663.1563 MAE train: 6.68537 MAE test: 6.45416 Epoch 43/1000 110/110 [==============================] - 1s 6ms/step - loss: 609.6542 MAE train: 10.90723 MAE test: 10.28778 Epoch 44/1000 110/110 [==============================] - 1s 6ms/step - loss: 711.0805 MAE train: 6.24676 MAE test: 5.05785 Epoch 45/1000 110/110 [==============================] - 1s 6ms/step - loss: 564.7139 MAE train: 6.28988 MAE test: 4.06587 Epoch 46/1000 110/110 [==============================] - 1s 5ms/step - loss: 595.9222 MAE train: 8.50904 MAE test: 7.34465 Epoch 47/1000 110/110 [==============================] - 1s 6ms/step - loss: 757.4929 MAE train: 5.93183 MAE test: 5.19274 Epoch 48/1000 110/110 [==============================] - ETA: 0s - loss: 565.008 - 1s 6ms/step - loss: 569.3626 MAE train: 8.40654 MAE test: 7.01656 Epoch 49/1000 110/110 [==============================] - 1s 6ms/step - loss: 576.3763 MAE train: 9.58185 MAE test: 9.03955 Epoch 50/1000 110/110 [==============================] - 1s 6ms/step - loss: 634.7589 MAE train: 9.16613 MAE test: 7.17657 Epoch 51/1000 110/110 [==============================] - 1s 6ms/step - loss: 602.9331 MAE train: 6.5175 MAE test: 5.18624 Epoch 52/1000 110/110 [==============================] - 1s 6ms/step - loss: 608.3923 MAE train: 9.75431 MAE test: 7.64877 Epoch 53/1000 110/110 [==============================] - 1s 5ms/step - loss: 555.4568 MAE train: 6.69824 MAE test: 6.3297 Epoch 54/1000 110/110 [==============================] - 1s 6ms/step - loss: 545.4324 MAE train: 7.15037 MAE test: 6.84802 Epoch 55/1000 110/110 [==============================] - 1s 5ms/step - loss: 577.2234 MAE train: 7.93176 MAE test: 6.41611 Epoch 56/1000 110/110 [==============================] - 1s 5ms/step - loss: 542.1740 MAE train: 6.09077 MAE test: 4.62174 Epoch 57/1000 110/110 [==============================] - 1s 6ms/step - loss: 642.0947 MAE train: 6.30786 MAE test: 5.53994 Epoch 58/1000 110/110 [==============================] - 1s 6ms/step - loss: 548.0550 MAE train: 7.10483 MAE test: 6.75162 Epoch 59/1000 110/110 [==============================] - 1s 5ms/step - loss: 571.5150 MAE train: 7.09908 MAE test: 5.31102 Epoch 60/1000 110/110 [==============================] - 1s 6ms/step - loss: 580.7704 MAE train: 5.99511 MAE test: 5.15384 Epoch 61/1000 110/110 [==============================] - 1s 5ms/step - loss: 589.7680 MAE train: 7.76611 MAE test: 7.69361 Epoch 62/1000 110/110 [==============================] - 1s 6ms/step - loss: 538.7684 MAE train: 7.66223 MAE test: 6.96845 Epoch 63/1000 110/110 [==============================] - 1s 6ms/step - loss: 507.7687 MAE train: 6.20674 MAE test: 5.01945 Epoch 64/1000 110/110 [==============================] - 1s 6ms/step - loss: 564.3373 MAE train: 7.21023 MAE test: 5.56935 Epoch 65/1000 110/110 [==============================] - 1s 5ms/step - loss: 524.9993 MAE train: 6.56309 MAE test: 5.95133 Epoch 66/1000 110/110 [==============================] - 1s 6ms/step - loss: 564.9947 MAE train: 8.15461 MAE test: 6.78805 Epoch 67/1000 110/110 [==============================] - 1s 6ms/step - loss: 508.1738 MAE train: 7.79827 MAE test: 6.28257 Epoch 68/1000 110/110 [==============================] - 1s 6ms/step - loss: 557.1159 MAE train: 7.57019 MAE test: 6.32809 Epoch 69/1000 110/110 [==============================] - 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1s 5ms/step - loss: 137.5103 MAE train: 2.43268 MAE test: 2.3265 Epoch 945/1000 110/110 [==============================] - 1s 5ms/step - loss: 150.2040 MAE train: 2.52436 MAE test: 2.56959 Epoch 946/1000 110/110 [==============================] - 1s 5ms/step - loss: 151.4475 MAE train: 2.67876 MAE test: 2.72493 Epoch 947/1000 110/110 [==============================] - 1s 5ms/step - loss: 135.5926 MAE train: 2.47839 MAE test: 2.11653 Epoch 948/1000 110/110 [==============================] - 1s 5ms/step - loss: 139.8340A: 0s - loss: 140.126 MAE train: 2.42245 MAE test: 2.43536 Epoch 949/1000 110/110 [==============================] - 1s 5ms/step - loss: 147.9918 MAE train: 3.13282 MAE test: 2.97077 Epoch 950/1000 110/110 [==============================] - 1s 5ms/step - loss: 148.4390 MAE train: 2.59894 MAE test: 2.19546 Epoch 951/1000 110/110 [==============================] - 1s 6ms/step - loss: 135.6499 MAE train: 2.47918 MAE test: 2.33181 Epoch 952/1000 110/110 [==============================] - 1s 6ms/step - loss: 131.7069 MAE train: 2.72992 MAE test: 2.94324 Epoch 953/1000 110/110 [==============================] - 1s 6ms/step - loss: 136.8338 MAE train: 3.01806 MAE test: 2.90327 Epoch 954/1000 110/110 [==============================] - 1s 5ms/step - loss: 143.0780 MAE train: 2.54538 MAE test: 2.55717 Epoch 955/1000 110/110 [==============================] - 1s 6ms/step - loss: 160.4122 MAE train: 2.37027 MAE test: 2.35454 Epoch 956/1000 110/110 [==============================] - 1s 5ms/step - loss: 148.3750 MAE train: 2.8456 MAE test: 2.7571 Epoch 957/1000 110/110 [==============================] - 1s 5ms/step - loss: 135.1745 MAE train: 2.50984 MAE test: 2.68846 Epoch 958/1000 110/110 [==============================] - 1s 6ms/step - loss: 144.8989 MAE train: 2.53694 MAE test: 2.44409 Epoch 959/1000 110/110 [==============================] - 1s 6ms/step - loss: 133.3156 MAE train: 3.07212 MAE test: 2.98093 Epoch 960/1000 110/110 [==============================] - 1s 6ms/step - loss: 154.5550 MAE train: 2.8632 MAE test: 2.81696 Epoch 961/1000 110/110 [==============================] - 1s 6ms/step - loss: 148.3616 MAE train: 2.42974 MAE test: 2.11664 Epoch 962/1000 110/110 [==============================] - 1s 5ms/step - loss: 127.8701 MAE train: 2.88536 MAE test: 2.94564 Epoch 963/1000 110/110 [==============================] - 1s 6ms/step - loss: 145.8089 MAE train: 2.57799 MAE test: 2.89649 Epoch 964/1000 110/110 [==============================] - 1s 6ms/step - loss: 125.3703 MAE train: 2.35681 MAE test: 2.30086 Epoch 965/1000 110/110 [==============================] - 1s 6ms/step - loss: 133.2721 MAE train: 2.5472 MAE test: 2.1501 Epoch 966/1000 110/110 [==============================] - 1s 6ms/step - loss: 160.3187 MAE train: 2.58766 MAE test: 2.66006 Epoch 967/1000 110/110 [==============================] - 1s 5ms/step - loss: 145.5237 MAE train: 2.42067 MAE test: 2.25438 Epoch 968/1000 110/110 [==============================] - 1s 6ms/step - loss: 144.1978 MAE train: 2.44586 MAE test: 2.59425 Epoch 969/1000 110/110 [==============================] - 1s 6ms/step - loss: 132.3821 MAE train: 2.345 MAE test: 2.28427 Epoch 970/1000 110/110 [==============================] - 1s 6ms/step - loss: 143.9277 MAE train: 2.48335 MAE test: 2.49234 Epoch 971/1000 110/110 [==============================] - 1s 6ms/step - loss: 127.1619 MAE train: 2.93538 MAE test: 2.86826 Epoch 972/1000 110/110 [==============================] - 1s 5ms/step - loss: 148.6607 MAE train: 2.47876 MAE test: 2.63399 Epoch 973/1000 110/110 [==============================] - 1s 6ms/step - loss: 126.1772 MAE train: 2.23661 MAE test: 2.25497 Epoch 974/1000 110/110 [==============================] - 1s 6ms/step - loss: 146.2727 MAE train: 2.43619 MAE test: 2.16652 Epoch 975/1000 110/110 [==============================] - 1s 5ms/step - loss: 137.6542 MAE train: 2.56339 MAE test: 2.68273 Epoch 976/1000 110/110 [==============================] - 1s 5ms/step - loss: 139.1322 MAE train: 2.92882 MAE test: 2.73774 Epoch 977/1000 110/110 [==============================] - 1s 5ms/step - loss: 157.0474 MAE train: 2.42185 MAE test: 2.36416 Epoch 978/1000 110/110 [==============================] - 1s 6ms/step - loss: 140.4591 MAE train: 2.39292 MAE test: 2.56206 Epoch 979/1000 110/110 [==============================] - 1s 5ms/step - loss: 139.7182 MAE train: 2.51889 MAE test: 2.66143 Epoch 980/1000 110/110 [==============================] - 1s 6ms/step - loss: 145.6806 MAE train: 2.74323 MAE test: 3.09616 Epoch 981/1000 110/110 [==============================] - 1s 5ms/step - loss: 167.6346 MAE train: 2.52885 MAE test: 2.24065 Epoch 982/1000 110/110 [==============================] - 1s 6ms/step - loss: 143.4672 MAE train: 2.8392 MAE test: 2.63147 Epoch 983/1000 110/110 [==============================] - 1s 5ms/step - loss: 155.4734 MAE train: 2.71136 MAE test: 2.5694 Epoch 984/1000 110/110 [==============================] - 1s 6ms/step - loss: 143.3349 MAE train: 2.78612 MAE test: 2.9326 Epoch 985/1000 110/110 [==============================] - 1s 5ms/step - loss: 128.9482 MAE train: 2.44778 MAE test: 2.51672 Epoch 986/1000 110/110 [==============================] - 1s 5ms/step - loss: 152.6687 MAE train: 2.55619 MAE test: 2.69552 Epoch 987/1000 110/110 [==============================] - 1s 6ms/step - loss: 131.4025 MAE train: 2.46243 MAE test: 2.29631 Epoch 988/1000 110/110 [==============================] - 1s 6ms/step - loss: 156.2193 MAE train: 2.52145 MAE test: 2.49849 Epoch 989/1000 110/110 [==============================] - 1s 5ms/step - loss: 144.8567 MAE train: 2.45039 MAE test: 2.50265 Epoch 990/1000 110/110 [==============================] - 1s 5ms/step - loss: 141.1487 MAE train: 2.81052 MAE test: 2.96067 Epoch 991/1000 110/110 [==============================] - 1s 5ms/step - loss: 136.2088 MAE train: 2.77214 MAE test: 2.84207 Epoch 992/1000 110/110 [==============================] - 1s 5ms/step - loss: 150.2306 MAE train: 2.34642 MAE test: 2.49995 Epoch 993/1000 110/110 [==============================] - 1s 5ms/step - loss: 157.1958 MAE train: 2.32751 MAE test: 2.30238 Epoch 994/1000 110/110 [==============================] - 1s 5ms/step - loss: 151.3743 MAE train: 2.56315 MAE test: 2.73623 Epoch 995/1000 110/110 [==============================] - 1s 5ms/step - loss: 152.9550 MAE train: 2.56281 MAE test: 2.55934 Epoch 996/1000 110/110 [==============================] - 1s 5ms/step - loss: 134.9298 MAE train: 2.40017 MAE test: 2.342 Epoch 997/1000 110/110 [==============================] - 1s 5ms/step - loss: 136.8814 MAE train: 2.6562 MAE test: 2.42653 Epoch 998/1000 110/110 [==============================] - 1s 5ms/step - loss: 148.4753 MAE train: 2.82262 MAE test: 2.69575 Epoch 999/1000 110/110 [==============================] - 1s 6ms/step - loss: 145.0945 MAE train: 2.67193 MAE test: 2.82833 Epoch 1000/1000 110/110 [==============================] - 1s 6ms/step - loss: 147.0421 MAE train: 3.002 MAE test: 2.7679
plt.figure(figsize=(20,20))
plt.plot(history_2.history['MAE_score_train'])
plt.plot(history_2.history['MAE_score_val'])
plt.ylabel('MAE', fontsize=30)
plt.xlabel('Epoch', fontsize=30)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.legend(['Treino', 'Teste'], loc='upper right', fontsize=25)
plt.savefig('model_zone_2.png')
plt.show()
history_total = model_total.fit(X_total, y_total, epochs=1000, batch_size=32, workers=-1, use_multiprocessing=True,
callbacks=[MAEHistory(train=(X_zone2, y_zone2), robot=robot, scaler=scaler_X2, validation=(test_set_input1 + test_set_input2, test_set_output1 + test_set_output2))])
Epoch 1/1000 220/220 [==============================] - 2s 5ms/step - loss: 5057.7331 MAE train: 22.49709 MAE test: 20.64684 Epoch 2/1000 220/220 [==============================] - 1s 5ms/step - loss: 2538.0451: 0s - loss: 2550. MAE train: 25.06177 MAE test: 25.55232 Epoch 3/1000 220/220 [==============================] - 1s 5ms/step - loss: 2452.8124 MAE train: 17.27451 MAE test: 15.78177 Epoch 4/1000 220/220 [==============================] - 1s 5ms/step - loss: 2045.5003 MAE train: 15.78102 MAE test: 14.20338 Epoch 5/1000 220/220 [==============================] - 1s 5ms/step - loss: 1950.6163 MAE train: 18.1612 MAE test: 17.7216 Epoch 6/1000 220/220 [==============================] - 1s 5ms/step - loss: 1818.1597 MAE train: 21.55599 MAE test: 19.09777 Epoch 7/1000 220/220 [==============================] - 1s 5ms/step - loss: 1724.4298 MAE train: 16.11938 MAE test: 13.6029 Epoch 8/1000 220/220 [==============================] - 1s 5ms/step - loss: 1573.6931 MAE train: 14.41189 MAE test: 14.86822 Epoch 9/1000 220/220 [==============================] - 1s 6ms/step - loss: 1530.3373 MAE train: 16.84131 MAE test: 15.53696 Epoch 10/1000 220/220 [==============================] - 1s 5ms/step - loss: 1510.5273 MAE train: 16.15588 MAE test: 18.02197 Epoch 11/1000 220/220 [==============================] - 1s 6ms/step - loss: 1445.5813 MAE train: 18.06994 MAE test: 14.39041 Epoch 12/1000 220/220 [==============================] - 1s 6ms/step - loss: 1478.6038 MAE train: 12.94488 MAE test: 10.96155 Epoch 13/1000 220/220 [==============================] - 1s 6ms/step - loss: 1487.6568 MAE train: 14.76557 MAE test: 12.5402 Epoch 14/1000 220/220 [==============================] - 1s 6ms/step - loss: 1432.8148 MAE train: 13.00385 MAE test: 9.40102 Epoch 15/1000 220/220 [==============================] - 1s 5ms/step - loss: 1417.8818 MAE train: 11.2403 MAE test: 10.94648 Epoch 16/1000 220/220 [==============================] - 1s 5ms/step - loss: 1422.5131 MAE train: 14.22215 MAE test: 13.1243 Epoch 17/1000 220/220 [==============================] - 1s 5ms/step - loss: 1351.2515 MAE train: 16.6449 MAE test: 15.53553 Epoch 18/1000 220/220 [==============================] - 1s 5ms/step - loss: 1306.1153 MAE train: 14.14831 MAE test: 11.15681 Epoch 19/1000 220/220 [==============================] - 1s 5ms/step - loss: 1288.4996 MAE train: 10.97355 MAE test: 9.40436 Epoch 20/1000 220/220 [==============================] - 1s 5ms/step - loss: 1296.6354 MAE train: 12.11359 MAE test: 10.20695 Epoch 21/1000 220/220 [==============================] - 1s 5ms/step - loss: 1247.2266 MAE train: 11.53493 MAE test: 10.07202 Epoch 22/1000 220/220 [==============================] - 1s 5ms/step - loss: 1200.6439 MAE train: 9.66868 MAE test: 8.41617 Epoch 23/1000 220/220 [==============================] - 1s 6ms/step - loss: 1146.0601 MAE train: 10.04818 MAE test: 9.36671 Epoch 24/1000 220/220 [==============================] - 1s 6ms/step - loss: 1200.4558 MAE train: 11.26059 MAE test: 9.4813 Epoch 25/1000 220/220 [==============================] - 1s 5ms/step - loss: 1277.6386 MAE train: 10.99527 MAE test: 8.77208 Epoch 26/1000 220/220 [==============================] - 1s 5ms/step - loss: 1285.4143 MAE train: 12.22861 MAE test: 11.42745 Epoch 27/1000 220/220 [==============================] - 1s 5ms/step - loss: 1171.8080 MAE train: 9.8332 MAE test: 7.31754 Epoch 28/1000 220/220 [==============================] - 1s 5ms/step - loss: 1174.2820 MAE train: 9.64577 MAE test: 7.49686 Epoch 29/1000 220/220 [==============================] - 1s 5ms/step - loss: 1138.6503 MAE train: 11.2088 MAE test: 8.92154 Epoch 30/1000 220/220 [==============================] - 1s 5ms/step - loss: 1216.6126 MAE train: 9.96527 MAE test: 7.71203 Epoch 31/1000 220/220 [==============================] - 1s 6ms/step - loss: 1125.0377 MAE train: 12.08087 MAE test: 10.83591 Epoch 32/1000 220/220 [==============================] - 1s 5ms/step - loss: 1069.1455 MAE train: 13.32313 MAE test: 13.14629 Epoch 33/1000 220/220 [==============================] - 1s 5ms/step - loss: 1154.6969 MAE train: 10.72947 MAE test: 9.04701 Epoch 34/1000 220/220 [==============================] - 1s 5ms/step - loss: 1145.8680 MAE train: 11.53855 MAE test: 8.2317 Epoch 35/1000 220/220 [==============================] - 1s 5ms/step - loss: 1123.2161 MAE train: 10.28365 MAE test: 7.8893 Epoch 36/1000 220/220 [==============================] - 1s 5ms/step - loss: 1155.4339 MAE train: 9.25452 MAE test: 8.59099 Epoch 37/1000 220/220 [==============================] - 1s 5ms/step - loss: 1103.2046 MAE train: 12.29738 MAE test: 10.71705 Epoch 38/1000 220/220 [==============================] - 1s 6ms/step - loss: 1071.1784 MAE train: 9.84192 MAE test: 8.55384 Epoch 39/1000 220/220 [==============================] - 1s 5ms/step - loss: 1047.8211 MAE train: 8.68554 MAE test: 7.01715 Epoch 40/1000 220/220 [==============================] - 1s 5ms/step - loss: 1078.2461 MAE train: 9.51469 MAE test: 8.17442 Epoch 41/1000 220/220 [==============================] - 1s 6ms/step - loss: 997.7360 MAE train: 8.22797 MAE test: 6.25391 Epoch 42/1000 220/220 [==============================] - 1s 6ms/step - loss: 1024.5078 MAE train: 8.84543 MAE test: 6.492 Epoch 43/1000 220/220 [==============================] - 1s 5ms/step - loss: 1011.5203: MAE train: 8.96393 MAE test: 7.27711 Epoch 44/1000 220/220 [==============================] - 1s 5ms/step - loss: 1067.1147 MAE train: 9.50508 MAE test: 7.82421 Epoch 45/1000 220/220 [==============================] - 1s 5ms/step - loss: 1016.7264 MAE train: 7.87025 MAE test: 7.6534 Epoch 46/1000 220/220 [==============================] - 1s 5ms/step - loss: 1030.0282 MAE train: 8.68213 MAE test: 7.85886 Epoch 47/1000 220/220 [==============================] - 1s 6ms/step - loss: 1033.8483 MAE train: 9.07199 MAE test: 7.08691 Epoch 48/1000 220/220 [==============================] - 1s 5ms/step - loss: 1084.3020 MAE train: 9.39471 MAE test: 7.75126 Epoch 49/1000 220/220 [==============================] - 1s 5ms/step - loss: 1056.5214 MAE train: 9.99533 MAE test: 8.3151 Epoch 50/1000 220/220 [==============================] - 1s 5ms/step - loss: 983.6042A: 0s - loss: 982.80 MAE train: 7.34542 MAE test: 6.20126 Epoch 51/1000 220/220 [==============================] - 1s 5ms/step - loss: 991.8867 MAE train: 11.37761 MAE test: 10.02259 Epoch 52/1000 220/220 [==============================] - 1s 5ms/step - loss: 993.3060 MAE train: 9.39157 MAE test: 8.27499 Epoch 53/1000 220/220 [==============================] - 1s 5ms/step - loss: 998.8373 MAE train: 9.32424 MAE test: 7.62861 Epoch 54/1000 220/220 [==============================] - 1s 5ms/step - loss: 1039.0305 MAE train: 7.64757 MAE test: 6.67201 Epoch 55/1000 220/220 [==============================] - 1s 5ms/step - loss: 986.4633 MAE train: 8.4832 MAE test: 6.93047 Epoch 56/1000 220/220 [==============================] - 1s 5ms/step - loss: 941.2950 MAE train: 8.64831 MAE test: 7.45406 Epoch 57/1000 220/220 [==============================] - 1s 5ms/step - loss: 960.1462 MAE train: 10.28448 MAE test: 8.67275 Epoch 58/1000 220/220 [==============================] - 1s 5ms/step - loss: 978.4056 MAE train: 9.29017 MAE test: 8.74409 Epoch 59/1000 220/220 [==============================] - 1s 5ms/step - loss: 970.4420 MAE train: 10.6834 MAE test: 8.4049 Epoch 60/1000 220/220 [==============================] - 1s 5ms/step - loss: 967.5618 MAE train: 8.47894 MAE test: 7.1852 Epoch 61/1000 220/220 [==============================] - 1s 5ms/step - loss: 937.9888 MAE train: 8.51954 MAE test: 5.52476 Epoch 62/1000 220/220 [==============================] - 1s 5ms/step - loss: 956.5886 MAE train: 10.61406 MAE test: 9.86587 Epoch 63/1000 220/220 [==============================] - 1s 5ms/step - loss: 988.6996 MAE train: 7.29091 MAE test: 6.38171 Epoch 64/1000 220/220 [==============================] - 1s 5ms/step - loss: 983.4872 MAE train: 7.62885 MAE test: 6.91582 Epoch 65/1000 220/220 [==============================] - 1s 5ms/step - loss: 1024.5314 MAE train: 9.08555 MAE test: 7.5305 Epoch 66/1000 220/220 [==============================] - 1s 5ms/step - loss: 982.3510 MAE train: 8.04549 MAE test: 6.40113 Epoch 67/1000 220/220 [==============================] - 1s 5ms/step - loss: 970.7068 MAE train: 9.67845 MAE test: 7.86692 Epoch 68/1000 220/220 [==============================] - 1s 5ms/step - loss: 944.7021 MAE train: 7.71077 MAE test: 7.67378 Epoch 69/1000 220/220 [==============================] - 1s 6ms/step - loss: 911.8501 MAE train: 7.4392 MAE test: 5.77835 Epoch 70/1000 220/220 [==============================] - 1s 5ms/step - loss: 949.3899 MAE train: 6.91317 MAE test: 5.47695 Epoch 71/1000 220/220 [==============================] - 1s 5ms/step - loss: 984.2439 MAE train: 7.90268 MAE test: 6.29801 Epoch 72/1000 220/220 [==============================] - 1s 5ms/step - loss: 911.7674 MAE train: 7.98204 MAE test: 7.0889 Epoch 73/1000 220/220 [==============================] - 1s 6ms/step - loss: 910.9503 MAE train: 7.21546 MAE test: 6.38472 Epoch 74/1000 220/220 [==============================] - 1s 6ms/step - loss: 927.1693 MAE train: 7.7294 MAE test: 6.72994 Epoch 75/1000 220/220 [==============================] - 1s 5ms/step - loss: 939.1862 MAE train: 7.92632 MAE test: 6.61026 Epoch 76/1000 220/220 [==============================] - 1s 5ms/step - loss: 995.1321 MAE train: 7.62545 MAE test: 6.79062 Epoch 77/1000 220/220 [==============================] - 1s 6ms/step - loss: 935.4748 MAE train: 7.16318 MAE test: 6.05156 Epoch 78/1000 220/220 [==============================] - 1s 5ms/step - loss: 890.7095 MAE train: 7.31846 MAE test: 6.56999 Epoch 79/1000 220/220 [==============================] - 1s 5ms/step - loss: 915.5744 MAE train: 7.60307 MAE test: 5.59953 Epoch 80/1000 220/220 [==============================] - 1s 5ms/step - loss: 896.5923 MAE train: 8.66861 MAE test: 6.53649 Epoch 81/1000 220/220 [==============================] - 1s 5ms/step - loss: 926.8012 MAE train: 6.80123 MAE test: 5.80757 Epoch 82/1000 220/220 [==============================] - 1s 5ms/step - loss: 875.7702 MAE train: 7.72653 MAE test: 6.01659 Epoch 83/1000 220/220 [==============================] - 1s 5ms/step - loss: 892.0435A: 0s MAE train: 6.58466 MAE test: 5.42927 Epoch 84/1000 220/220 [==============================] - 1s 5ms/step - loss: 924.0491 MAE train: 6.90223 MAE test: 6.02594 Epoch 85/1000 220/220 [==============================] - 1s 5ms/step - loss: 901.6805 MAE train: 7.02268 MAE test: 6.44349 Epoch 86/1000 220/220 [==============================] - 1s 5ms/step - loss: 874.4156 MAE train: 6.58776 MAE test: 5.44141 Epoch 87/1000 220/220 [==============================] - 1s 5ms/step - loss: 898.7270 MAE train: 7.39603 MAE test: 4.88536 Epoch 88/1000 220/220 [==============================] - 1s 5ms/step - loss: 979.0684 MAE train: 9.32679 MAE test: 7.8752 Epoch 89/1000 220/220 [==============================] - 1s 5ms/step - loss: 874.4518 MAE train: 7.88486 MAE test: 6.75169 Epoch 90/1000 220/220 [==============================] - 1s 6ms/step - loss: 919.3690 MAE train: 7.576 MAE test: 6.05439 Epoch 91/1000 220/220 [==============================] - 1s 5ms/step - loss: 883.9973 MAE train: 7.72339 MAE test: 7.0318 Epoch 92/1000 220/220 [==============================] - 1s 5ms/step - loss: 917.5130 MAE train: 8.24276 MAE test: 7.40856 Epoch 93/1000 220/220 [==============================] - 1s 5ms/step - loss: 940.8145 MAE train: 6.16321 MAE test: 5.87768 Epoch 94/1000 220/220 [==============================] - 1s 6ms/step - loss: 863.9573 MAE train: 8.15286 MAE test: 8.2506 Epoch 95/1000 220/220 [==============================] - 1s 6ms/step - loss: 944.7458 MAE train: 6.98473 MAE test: 6.4014 Epoch 96/1000 220/220 [==============================] - 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loss: 715.0794 MAE train: 7.04121 MAE test: 6.61272 Epoch 602/1000 220/220 [==============================] - 2s 8ms/step - loss: 724.0834 MAE train: 6.96223 MAE test: 6.13086 Epoch 603/1000 220/220 [==============================] - 2s 7ms/step - loss: 686.5693 MAE train: 7.92204 MAE test: 7.50307 Epoch 604/1000 220/220 [==============================] - 2s 8ms/step - loss: 730.5254 MAE train: 7.20401 MAE test: 7.12016 Epoch 605/1000 220/220 [==============================] - 1s 7ms/step - loss: 717.7078 MAE train: 7.20402 MAE test: 6.8826 Epoch 606/1000 220/220 [==============================] - 2s 8ms/step - loss: 657.3609 MAE train: 7.58931 MAE test: 6.84357 Epoch 607/1000 220/220 [==============================] - 2s 8ms/step - loss: 698.6583 MAE train: 6.69321 MAE test: 6.49659 Epoch 608/1000 220/220 [==============================] - 1s 6ms/step - loss: 650.8060 MAE train: 6.35913 MAE test: 6.3508 Epoch 609/1000 220/220 [==============================] - 2s 7ms/step - loss: 713.1269 MAE train: 6.47115 MAE test: 6.459 Epoch 610/1000 220/220 [==============================] - 1s 5ms/step - loss: 705.9844 MAE train: 6.91579 MAE test: 6.3284 Epoch 611/1000 220/220 [==============================] - 1s 6ms/step - loss: 736.7659 MAE train: 6.63321 MAE test: 6.70605 Epoch 612/1000 220/220 [==============================] - 1s 6ms/step - loss: 724.4853 MAE train: 7.18395 MAE test: 7.56731 Epoch 613/1000 220/220 [==============================] - 2s 8ms/step - loss: 741.4440 MAE train: 7.76745 MAE test: 7.66131 Epoch 614/1000 220/220 [==============================] - 2s 7ms/step - loss: 706.9591 MAE train: 6.64349 MAE test: 6.10208 Epoch 615/1000 220/220 [==============================] - 2s 8ms/step - loss: 699.5574 MAE train: 6.89261 MAE test: 6.55476 Epoch 616/1000 220/220 [==============================] - 2s 7ms/step - loss: 698.8072 MAE train: 8.11345 MAE test: 8.03886 Epoch 617/1000 220/220 [==============================] - 2s 7ms/step - loss: 674.8049 MAE train: 7.07428 MAE test: 7.3151 Epoch 618/1000 220/220 [==============================] - 2s 8ms/step - loss: 719.1747 MAE train: 6.32863 MAE test: 6.18696 Epoch 619/1000 220/220 [==============================] - 2s 8ms/step - loss: 769.7566 MAE train: 7.1395 MAE test: 7.22882 Epoch 620/1000 220/220 [==============================] - 2s 10ms/step - loss: 727.7498 MAE train: 6.42152 MAE test: 6.23619 Epoch 621/1000 220/220 [==============================] - 2s 11ms/step - loss: 689.8211 MAE train: 6.18997 MAE test: 6.19476 Epoch 622/1000 220/220 [==============================] - 3s 12ms/step - loss: 732.9196 MAE train: 6.93107 MAE test: 6.99674 Epoch 623/1000 220/220 [==============================] - 2s 7ms/step - loss: 707.0023 MAE train: 6.46147 MAE test: 6.328 Epoch 624/1000 220/220 [==============================] - 1s 6ms/step - loss: 730.2752 MAE train: 5.66565 MAE test: 5.70673 Epoch 625/1000 220/220 [==============================] - 1s 6ms/step - loss: 704.1658 MAE train: 7.43137 MAE test: 6.65018 Epoch 626/1000 220/220 [==============================] - 1s 6ms/step - loss: 688.2879 MAE train: 6.24371 MAE test: 6.3546 Epoch 627/1000 220/220 [==============================] - 1s 6ms/step - loss: 715.0000 MAE train: 7.34458 MAE test: 6.79372 Epoch 628/1000 220/220 [==============================] - 1s 7ms/step - loss: 709.5799 MAE train: 6.34492 MAE test: 6.24683 Epoch 629/1000 220/220 [==============================] - 2s 7ms/step - loss: 672.3752 MAE train: 7.22962 MAE test: 7.00562 Epoch 630/1000 220/220 [==============================] - 2s 8ms/step - loss: 749.6508 MAE train: 6.36844 MAE test: 6.05165 Epoch 631/1000 220/220 [==============================] - 2s 7ms/step - loss: 682.2479 MAE train: 7.26911 MAE test: 7.2917 Epoch 632/1000 220/220 [==============================] - 2s 7ms/step - loss: 681.8172 MAE train: 7.70513 MAE test: 7.66252 Epoch 633/1000 220/220 [==============================] - 2s 8ms/step - loss: 659.0847 MAE train: 6.04017 MAE test: 6.09733 Epoch 634/1000 220/220 [==============================] - 1s 6ms/step - loss: 715.3701 MAE train: 6.92363 MAE test: 6.68992 Epoch 635/1000 220/220 [==============================] - 2s 7ms/step - loss: 716.4079 MAE train: 7.3657 MAE test: 7.4145 Epoch 636/1000 220/220 [==============================] - 2s 8ms/step - loss: 725.0966 MAE train: 7.1124 MAE test: 7.10471 Epoch 637/1000 220/220 [==============================] - 2s 8ms/step - loss: 720.6128 MAE train: 6.89685 MAE test: 6.38892 Epoch 638/1000 220/220 [==============================] - 2s 7ms/step - loss: 691.3039 MAE train: 6.72187 MAE test: 6.81586 Epoch 639/1000 220/220 [==============================] - 2s 7ms/step - loss: 735.7105 MAE train: 6.97117 MAE test: 6.88118 Epoch 640/1000 220/220 [==============================] - 2s 8ms/step - loss: 774.4067 MAE train: 7.45854 MAE test: 7.22325 Epoch 641/1000 220/220 [==============================] - 2s 7ms/step - loss: 694.4585 MAE train: 7.3186 MAE test: 6.94677 Epoch 642/1000 220/220 [==============================] - 2s 8ms/step - loss: 667.8588 MAE train: 7.75862 MAE test: 7.3529 Epoch 643/1000 220/220 [==============================] - 2s 9ms/step - loss: 749.5530 MAE train: 6.17887 MAE test: 6.44718 Epoch 644/1000 220/220 [==============================] - 1s 6ms/step - loss: 730.1900 MAE train: 6.7354 MAE test: 6.82298 Epoch 645/1000 220/220 [==============================] - 1s 5ms/step - loss: 739.4230 MAE train: 7.26903 MAE test: 6.86251 Epoch 646/1000 220/220 [==============================] - 1s 6ms/step - loss: 692.4525 MAE train: 7.56775 MAE test: 7.13079 Epoch 647/1000 220/220 [==============================] - 1s 6ms/step - loss: 683.1052 MAE train: 8.02223 MAE test: 7.60941 Epoch 648/1000 220/220 [==============================] - 1s 5ms/step - loss: 699.9689 MAE train: 6.56465 MAE test: 6.48507 Epoch 649/1000 220/220 [==============================] - 1s 5ms/step - loss: 728.1474 MAE train: 6.62265 MAE test: 6.60071 Epoch 650/1000 220/220 [==============================] - 1s 6ms/step - loss: 689.1750 MAE train: 7.32726 MAE test: 6.85717 Epoch 651/1000 220/220 [==============================] - 1s 6ms/step - loss: 716.4518 MAE train: 7.22029 MAE test: 6.96638 Epoch 652/1000 220/220 [==============================] - 1s 6ms/step - loss: 724.1387 MAE train: 6.60479 MAE test: 6.41467 Epoch 653/1000 220/220 [==============================] - 1s 5ms/step - loss: 685.0369 MAE train: 7.59958 MAE test: 7.11983 Epoch 654/1000 220/220 [==============================] - 1s 6ms/step - loss: 690.0829 MAE train: 6.61022 MAE test: 6.63352 Epoch 655/1000 220/220 [==============================] - 1s 6ms/step - loss: 701.2785 MAE train: 7.60749 MAE test: 7.17359 Epoch 656/1000 220/220 [==============================] - 1s 6ms/step - loss: 741.6233 MAE train: 7.26959 MAE test: 7.45811 Epoch 657/1000 220/220 [==============================] - 1s 6ms/step - loss: 699.6970 MAE train: 7.70856 MAE test: 7.76305 Epoch 658/1000 220/220 [==============================] - 1s 6ms/step - loss: 731.5056 MAE train: 7.07151 MAE test: 7.37299 Epoch 659/1000 220/220 [==============================] - 3s 16ms/step - loss: 697.6150 MAE train: 7.38885 MAE test: 6.83686 Epoch 660/1000 220/220 [==============================] - 2s 9ms/step - loss: 696.5710 MAE train: 6.46387 MAE test: 6.45354 Epoch 661/1000 220/220 [==============================] - 2s 8ms/step - loss: 697.4791 MAE train: 8.36395 MAE test: 7.9663 Epoch 662/1000 220/220 [==============================] - 1s 6ms/step - loss: 763.9402 MAE train: 6.6724 MAE test: 6.62727 Epoch 663/1000 220/220 [==============================] - 1s 6ms/step - loss: 718.7500 MAE train: 6.84826 MAE test: 7.09582 Epoch 664/1000 220/220 [==============================] - 1s 6ms/step - loss: 730.4792 MAE train: 6.139 MAE test: 6.07771 Epoch 665/1000 220/220 [==============================] - 1s 6ms/step - loss: 756.0440 MAE train: 8.06183 MAE test: 7.69705 Epoch 666/1000 220/220 [==============================] - 1s 6ms/step - loss: 685.5083 MAE train: 7.51146 MAE test: 7.62703 Epoch 667/1000 220/220 [==============================] - 1s 6ms/step - loss: 681.1967 MAE train: 6.33539 MAE test: 6.34774 Epoch 668/1000 220/220 [==============================] - 1s 6ms/step - loss: 711.2986 MAE train: 8.06474 MAE test: 7.7512 Epoch 669/1000 220/220 [==============================] - ETA: 0s - loss: 755.208 - 1s 6ms/step - loss: 753.9385 MAE train: 6.18256 MAE test: 6.00822 Epoch 670/1000 220/220 [==============================] - 1s 6ms/step - loss: 700.4027 MAE train: 7.03383 MAE test: 6.94524 Epoch 671/1000 220/220 [==============================] - 1s 6ms/step - loss: 676.6875 MAE train: 7.40628 MAE test: 7.37883 Epoch 672/1000 220/220 [==============================] - 1s 6ms/step - loss: 723.2696 MAE train: 7.22023 MAE test: 7.28981 Epoch 673/1000 220/220 [==============================] - 1s 6ms/step - loss: 695.0318 MAE train: 7.3876 MAE test: 7.18247 Epoch 674/1000 220/220 [==============================] - 1s 6ms/step - loss: 691.8933 MAE train: 6.87708 MAE test: 6.44993 Epoch 675/1000 220/220 [==============================] - 1s 6ms/step - loss: 691.2235 MAE train: 7.26673 MAE test: 7.57232 Epoch 676/1000 220/220 [==============================] - 1s 6ms/step - loss: 750.6456 MAE train: 7.87269 MAE test: 7.22147 Epoch 677/1000 220/220 [==============================] - 1s 7ms/step - loss: 720.2686 MAE train: 7.53348 MAE test: 7.41048 Epoch 678/1000 220/220 [==============================] - 1s 6ms/step - loss: 705.3891 MAE train: 7.25306 MAE test: 7.66724 Epoch 679/1000 220/220 [==============================] - 1s 6ms/step - loss: 675.3294 MAE train: 6.99528 MAE test: 6.69434 Epoch 680/1000 220/220 [==============================] - 2s 7ms/step - loss: 693.9369 MAE train: 8.05806 MAE test: 7.7648 Epoch 681/1000 220/220 [==============================] - 1s 6ms/step - loss: 704.4586 MAE train: 6.90992 MAE test: 6.77052 Epoch 682/1000 220/220 [==============================] - 1s 6ms/step - loss: 744.2753 MAE train: 6.35374 MAE test: 6.61623 Epoch 683/1000 220/220 [==============================] - 1s 6ms/step - loss: 707.6287 MAE train: 8.00142 MAE test: 7.51579 Epoch 684/1000 220/220 [==============================] - 1s 6ms/step - loss: 692.5987 MAE train: 6.72404 MAE test: 6.40448 Epoch 685/1000 220/220 [==============================] - 1s 6ms/step - loss: 705.9691 MAE train: 7.22094 MAE test: 6.87514 Epoch 686/1000 220/220 [==============================] - 1s 6ms/step - loss: 667.5327 MAE train: 7.39565 MAE test: 7.25283 Epoch 687/1000 220/220 [==============================] - 1s 7ms/step - loss: 723.0297 MAE train: 6.97014 MAE test: 6.70933 Epoch 688/1000 220/220 [==============================] - 1s 7ms/step - loss: 726.5398 MAE train: 7.5959 MAE test: 7.23504 Epoch 689/1000 220/220 [==============================] - 1s 7ms/step - loss: 714.4784 MAE train: 6.91233 MAE test: 6.22633 Epoch 690/1000 220/220 [==============================] - 2s 7ms/step - loss: 678.1531 MAE train: 8.07384 MAE test: 7.00724 Epoch 691/1000 220/220 [==============================] - 2s 7ms/step - loss: 716.1093 MAE train: 7.17619 MAE test: 6.80073 Epoch 692/1000 220/220 [==============================] - 2s 7ms/step - loss: 712.7347 MAE train: 6.87375 MAE test: 6.8129 Epoch 693/1000 220/220 [==============================] - 2s 7ms/step - loss: 695.5634 MAE train: 7.11518 MAE test: 7.17776 Epoch 694/1000 220/220 [==============================] - 2s 7ms/step - loss: 743.1255 MAE train: 6.71684 MAE test: 6.62882 Epoch 695/1000 220/220 [==============================] - 2s 7ms/step - loss: 692.8143 MAE train: 6.52047 MAE test: 6.56901 Epoch 696/1000 220/220 [==============================] - 2s 7ms/step - loss: 683.7366 MAE train: 7.29482 MAE test: 7.33593 Epoch 697/1000 220/220 [==============================] - 1s 7ms/step - loss: 726.6923 MAE train: 6.84692 MAE test: 7.156 Epoch 698/1000 220/220 [==============================] - 2s 8ms/step - loss: 769.5012A: 0s - loss: MAE train: 6.97871 MAE test: 7.0045 Epoch 699/1000 220/220 [==============================] - 1s 7ms/step - loss: 713.8439 MAE train: 7.52287 MAE test: 7.26034 Epoch 700/1000 220/220 [==============================] - 2s 7ms/step - loss: 718.4592 MAE train: 7.1251 MAE test: 6.60233 Epoch 701/1000 220/220 [==============================] - 1s 7ms/step - loss: 696.9568 MAE train: 7.15397 MAE test: 7.24174 Epoch 702/1000 220/220 [==============================] - 2s 7ms/step - loss: 740.7427 MAE train: 7.12737 MAE test: 6.79542 Epoch 703/1000 220/220 [==============================] - 2s 7ms/step - loss: 699.6524 MAE train: 6.72127 MAE test: 6.39957 Epoch 704/1000 220/220 [==============================] - 1s 7ms/step - loss: 710.6278 MAE train: 6.74749 MAE test: 6.58266 Epoch 705/1000 220/220 [==============================] - 2s 7ms/step - loss: 733.3087 MAE train: 6.80447 MAE test: 6.75998 Epoch 706/1000 220/220 [==============================] - 2s 7ms/step - loss: 719.8758 MAE train: 7.34845 MAE test: 6.97886 Epoch 707/1000 220/220 [==============================] - 2s 7ms/step - loss: 710.5987 MAE train: 6.20878 MAE test: 6.08828 Epoch 708/1000 220/220 [==============================] - 1s 7ms/step - loss: 668.7258 MAE train: 7.00359 MAE test: 7.45751 Epoch 709/1000 220/220 [==============================] - 2s 7ms/step - loss: 707.3758 MAE train: 7.74577 MAE test: 7.78899 Epoch 710/1000 220/220 [==============================] - 2s 7ms/step - loss: 689.3442 MAE train: 8.44424 MAE test: 8.29908 Epoch 711/1000 220/220 [==============================] - 2s 7ms/step - loss: 740.6907 MAE train: 7.29779 MAE test: 6.86929 Epoch 712/1000 220/220 [==============================] - 2s 7ms/step - loss: 705.4221 MAE train: 7.27201 MAE test: 7.19023 Epoch 713/1000 220/220 [==============================] - 1s 7ms/step - loss: 732.7921 MAE train: 7.14437 MAE test: 6.25239 Epoch 714/1000 220/220 [==============================] - 2s 7ms/step - loss: 746.8128 MAE train: 8.09009 MAE test: 7.85816 Epoch 715/1000 220/220 [==============================] - 2s 7ms/step - loss: 750.7236 MAE train: 7.39985 MAE test: 7.43437 Epoch 716/1000 220/220 [==============================] - 2s 7ms/step - loss: 648.0338 MAE train: 7.51776 MAE test: 7.5032 Epoch 717/1000 220/220 [==============================] - 2s 7ms/step - loss: 714.3857 MAE train: 7.39311 MAE test: 7.33507 Epoch 718/1000 220/220 [==============================] - 1s 7ms/step - loss: 740.0846 MAE train: 6.33488 MAE test: 5.97561 Epoch 719/1000 220/220 [==============================] - 2s 7ms/step - loss: 716.3661 MAE train: 7.29288 MAE test: 7.36027 Epoch 720/1000 220/220 [==============================] - 1s 7ms/step - loss: 705.9949 MAE train: 7.09285 MAE test: 6.90523 Epoch 721/1000 220/220 [==============================] - 2s 7ms/step - loss: 654.1164 MAE train: 8.554 MAE test: 8.10145 Epoch 722/1000 220/220 [==============================] - 2s 7ms/step - loss: 732.1699 MAE train: 8.35723 MAE test: 7.6312 Epoch 723/1000 220/220 [==============================] - 2s 7ms/step - loss: 694.1160 MAE train: 6.7807 MAE test: 6.64244 Epoch 724/1000 220/220 [==============================] - 2s 7ms/step - loss: 654.6817 MAE train: 7.58828 MAE test: 7.29933 Epoch 725/1000 220/220 [==============================] - 1s 7ms/step - loss: 722.0039 MAE train: 8.37313 MAE test: 8.39744 Epoch 726/1000 220/220 [==============================] - 1s 6ms/step - loss: 684.2532 MAE train: 7.55896 MAE test: 7.44713 Epoch 727/1000 220/220 [==============================] - 2s 7ms/step - loss: 737.8563 MAE train: 6.99146 MAE test: 7.12819 Epoch 728/1000 220/220 [==============================] - 2s 7ms/step - loss: 688.4567 MAE train: 7.8203 MAE test: 7.43279 Epoch 729/1000 220/220 [==============================] - 2s 7ms/step - loss: 697.6827 MAE train: 6.33837 MAE test: 6.41879 Epoch 730/1000 220/220 [==============================] - 2s 7ms/step - loss: 679.1422 MAE train: 7.00213 MAE test: 6.95001 Epoch 731/1000 220/220 [==============================] - 2s 7ms/step - loss: 689.3387 MAE train: 7.56775 MAE test: 7.31683 Epoch 732/1000 220/220 [==============================] - 2s 7ms/step - loss: 663.6020 MAE train: 8.01486 MAE test: 7.62498 Epoch 733/1000 220/220 [==============================] - 2s 8ms/step - loss: 712.0992 MAE train: 7.22777 MAE test: 6.99349 Epoch 734/1000 220/220 [==============================] - 3s 12ms/step - loss: 684.2106 MAE train: 6.52946 MAE test: 6.43472 Epoch 735/1000 220/220 [==============================] - 2s 9ms/step - loss: 670.9250
plt.figure(figsize=(20,20))
plt.plot(history_total.history['MAE_score_train'])
plt.plot(history_total.history['MAE_score_val'])
plt.ylabel('MAE', fontsize=30)
plt.xlabel('Epoch', fontsize=30)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.legend(['Treino', 'Teste'], loc='upper right', fontsize=25)
plt.savefig('model_total.png')
plt.show()
model1.save("models/model1")
model2.save("models/model2")
model_total.save("models/model_total")
output = []
for X, Y, Z in input_pred:
output.append(model_total.predict(scaler_Xtotal.transform([[X, Y, Z]]))[0])
final_points = []
final_X_, final_Y_, final_Z_ = [], [], []
for theta1, theta2, theta3 in output:
robot.update_theta(0, theta1)
robot.update_theta(1, theta2)
robot.update_theta(2, theta3)
robot.forward_kinematics()
final_X_.append(robot.get_X())
final_Y_.append(robot.get_Y())
final_Z_.append(robot.get_Z())
final_points.append([robot.get_X(), robot.get_Y(), robot.get_Z()])
fig = go.Figure(data=go.Scatter3d(
x=final_X_, y=final_Y_, z=final_Z_,
marker=dict(
size=0,
color=3,
colorscale='Viridis',
),
line=dict(
color='orange',
width=2
),
name="Predito"
))
fig.add_trace(
go.Scatter3d(
x=final_X, y=final_Y, z=final_Z,
marker=dict(
size=0,
color=3,
colorscale='Viridis',
),
line=dict(
color='darkblue',
width=2
),
name="Real"
))
fig.update_layout(scene = dict(
xaxis = dict(nticks=8, range=[-110,110]),
yaxis = dict(nticks=8, range=[-110, 110]),
zaxis = dict(nticks=8, range=[0, 250])),
width=700, height=700)
fig.show()
output = []
for X, Y, Z in input_pred_2:
output.append(model_total.predict(scaler_Xtotal.transform([[X, Y, Z]]))[0])
points_final = []
final_X_, final_Y_, final_Z_ = [], [], []
for theta1, theta2, theta3 in output:
robot.update_theta(0, theta1)
robot.update_theta(1, theta2)
robot.update_theta(2, theta3)
robot.forward_kinematics()
final_X_.append(robot.get_X())
final_Y_.append(robot.get_Y())
final_Z_.append(robot.get_Z())
points_final.append([robot.get_X(), robot.get_Y(), robot.get_Z()])
fig = go.Figure(data=go.Scatter3d(
x=final_X_, y=final_Y_, z=final_Z_,
marker=dict(
size=0,
color=3,
colorscale='Viridis',
),
line=dict(
color='orange',
width=2
),
name="Predito"
))
fig.add_trace(
go.Scatter3d(
x=final_X_2, y=final_Y_2, z=final_Z_2,
marker=dict(
size=0,
color=3,
colorscale='Viridis',
),
line=dict(
color='darkblue',
width=2
),
name="Real"
))
fig.update_layout(scene = dict(
xaxis = dict(nticks=8, range=[-110,110]),
yaxis = dict(nticks=8, range=[-110, 110]),
zaxis = dict(nticks=8, range=[0, 200])),
width=700, height=700)
fig.show()
output = []
for X, Y, Z in input_pred:
if (X >= 0 and Y >= 0) or (X < 0 and Y < 0):
output.append(model1.predict(scaler_X1.transform([[X, Y, Z]]))[0])
else:
output.append(model2.predict(scaler_X2.transform([[X, Y, Z]]))[0])
final_points = []
final_X_, final_Y_, final_Z_ = [], [], []
for theta1, theta2, theta3 in output:
robot.update_theta(0, theta1)
robot.update_theta(1, theta2)
robot.update_theta(2, theta3)
robot.forward_kinematics()
final_X_.append(robot.get_X())
final_Y_.append(robot.get_Y())
final_Z_.append(robot.get_Z())
final_points.append([robot.get_X(), robot.get_Y(), robot.get_Z()])
fig = go.Figure(data=go.Scatter3d(
x=final_X_, y=final_Y_, z=final_Z_,
marker=dict(
size=0,
color=3,
colorscale='Viridis',
),
line=dict(
color='orange',
width=2
),
name="Predito"
))
fig.add_trace(
go.Scatter3d(
x=final_X, y=final_Y, z=final_Z,
marker=dict(
size=0,
color=3,
colorscale='Viridis',
),
line=dict(
color='darkblue',
width=2
),
name="Real"
))
fig.update_layout(scene = dict(
xaxis = dict(nticks=8, range=[-110,110]),
yaxis = dict(nticks=8, range=[-110, 110]),
zaxis = dict(nticks=8, range=[0, 250])),
width=700, height=600)
fig.show()
output = []
for X, Y, Z in input_pred_2:
if (X >= 0 and Y >= 0) or (X < 0 and Y < 0):
output.append(model1.predict(scaler_X1.transform([[X, Y, Z]]))[0])
else:
output.append(model2.predict(scaler_X2.transform([[X, Y, Z]]))[0])
points_final = []
final_X_, final_Y_, final_Z_ = [], [], []
for theta1, theta2, theta3 in output:
robot.update_theta(0, theta1)
robot.update_theta(1, theta2)
robot.update_theta(2, theta3)
robot.forward_kinematics()
final_X_.append(robot.get_X())
final_Y_.append(robot.get_Y())
final_Z_.append(robot.get_Z())
points_final.append([robot.get_X(), robot.get_Y(), robot.get_Z()])
fig = go.Figure(data=go.Scatter3d(
x=final_X_, y=final_Y_, z=final_Z_,
marker=dict(
size=0,
color=3,
colorscale='Viridis',
),
line=dict(
color='orange',
width=2
),
name="Predito"
))
fig.add_trace(
go.Scatter3d(
x=final_X_2, y=final_Y_2, z=final_Z_2,
marker=dict(
size=0,
color=3,
colorscale='Viridis',
),
line=dict(
color='darkblue',
width=2
),
name="Real"
))
fig.update_layout(scene = dict(
xaxis = dict(nticks=8, range=[-110,110]),
yaxis = dict(nticks=8, range=[-110, 110]),
zaxis = dict(nticks=8, range=[0, 200])),
width=700, height=700)
fig.show()